# DeepSeek vs Whisper (OpenAI)

DeepSeek records 372,761,591 monthly visits with +4.3% month-over-month growth and Freemium pricing. Whisper (OpenAI) records 0 monthly visits with — growth and Free pricing.

| Metric | [DeepSeek](https://openaitools.app/tool/deepseek) | [Whisper (OpenAI)](https://openaitools.app/tool/whisper-github) |
| :--- | :--- | :--- |
| Website URL | https://www.deepseek.com | https://github.com/openai/whisper |
| Categories | Large Language Models (LLMs), Open Source AI Models, AI API, AI Chatbot | AI Speech Recognition, AI Speech-to-Text, AI Transcriber, AI Translate |
| Pricing Model | Freemium | Free |
| Monthly Visits | 372,761,591 | 0 |
| MoM Growth Rate | +4.3% | — |
| Primary Market | China | Global |
| Direct Visits | 291,585,405 | 0 |
| Review Score | 3.8 | 5 |
| Saved Count | 44 | 3 |
| Supported Platforms | Website | Open Source |

## DeepSeek

DeepSeek (Hangzhou DeepSeek Artificial Intelligence Co., Ltd.) is a frontier AI research organization that disrupted the global LLM market by pairing open-weight transparency with breakthrough Mixture-of-Experts (MoE) and reinforcement-learning reasoning efficiency. Following the landmark releases of DeepSeek-V3 and the peer-reviewed DeepSeek-R1 reasoning model, DeepSeek introduced the DeepSeek-V4 generation in 2026—featuring a hybrid attention architecture and a native 1-million-token context window across DeepSeek-V4-Pro (1.6T total parameters, 49B active per token) and DeepSeek-V4.1-Flash (284B total parameters, 13B active per token).

Users can interact with DeepSeek in three ways: completely free through the consumer web portal (chat.deepseek.com) and mobile apps with one-click toggles for deep thinking and live web search; via DeepSeek's hosted, OpenAI-compatible developer API; or by self-hosting DeepSeek's open-weight checkpoints (including V3, R1, Prover-V2, and distilled Qwen/Llama variants) on private infrastructure. The models excel at competitive mathematics, algorithm design, repository-level software engineering, and structured data extraction, with automatic context caching on the API that slashes repeated prompt costs by over 95%.

In late 2026, DeepSeek remains the price-performance benchmark for developers and automation workflows. While the consumer chat interface remains free without a mandatory $20/month subscription tier, the developer API transitioned in late 2026 to two primary endpoints—`deepseek-flash` and `deepseek-v4-pro`—with time-of-day pricing. Off-peak API calls cost just $0.15 per million input tokens ($0.003 on cache hits) and $0.60 per million output tokens on Flash, and $0.66 input / $1.98 output on V4-Pro, making it a fraction of the cost of comparable Western frontier models.

**Pricing & Plans:** Free web and mobile chat app at chat.deepseek.com; pay-as-you-go OpenAI-compatible API with off-peak rates at $0.15/1M input ($0.003 cache hit) and $0.60/1M output for deepseek-flash (V4.1-Flash), and $0.66/1M input ($0.022 cache hit) and $1.98/1M output for deepseek-v4-pro (peak weekday UTC hours bill at 2x).

- **DeepSeek-V4 Hybrid Attention & 1M Context:** Leverages a sparse Mixture-of-Experts architecture (49B active parameters out of 1.6T on V4-Pro) to process up to 1 million tokens with minimal latency and memory overhead.
- **Transparent Chain-of-Thought Reasoning:** Pioneered by DeepSeek-R1 and integrated into V3.2 and V4 thinking modes, exposing step-by-step self-verification for complex math, logic, and code debugging.
- **Automatic Context Caching & Off-Peak API Pricing:** Drops input token costs to $0.003/1M on Flash and $0.022/1M on V4-Pro for cache hits, with 50% lower rates during off-peak UTC hours.
- **Free Consumer Web & Mobile Assistant:** Provides free access at chat.deepseek.com and on iOS/Android with document uploads, reasoning mode, and web search without paywalling core models.
- **Open-Weight Ecosystem & Self-Hosting Freedom:** Offers open weights for DeepSeek-V3, R1, Distill variants, and Prover models on Hugging Face and GitHub so enterprises can deploy within air-gapped environments.

**Strengths:** Completely free consumer web and mobile app with deep reasoning and web search included; Ultra-low API token rates ($0.15/$0.60 on Flash, $0.66/$1.98 on V4-Pro off-peak) with up to 98% savings on cached inputs; Drop-in OpenAI API compatibility makes switching or routing between models effortless; Strong open-weight lineage allows teams to self-host or fine-tune models without vendor lock-in

**Limitations:** Hosted API and web app store data on servers in China, prompting compliance restrictions for some Western enterprises and government agencies; Peak-hour API pricing (01:00–04:00 and 06:00–10:00 UTC on weekdays) doubles per-token costs compared to off-peak rates; Lacks native voice mode, video generation, and productivity suite integrations offered by ChatGPT and Google Gemini

[Profile (Markdown)](https://openaitools.app/tool/deepseek.md)

## Whisper (OpenAI)

Whisper was introduced by OpenAI as a general-purpose speech recognition model trained on a very large, diverse dataset of web audio. Instead of stacking separate acoustic, language and punctuation modules, it treats every speech task as a token prediction problem: transcription, translation, language identification and voice activity detection all share one Transformer encoder-decoder and are distinguished by special task tokens. That single-model design is why a straightforward `pip install` is enough to get multilingual transcription running on a laptop, and why Whisper has become a de facto baseline for open speech-to-text work.

The repository ships six model sizes — tiny, base, small, medium, large and a faster turbo variant — trading accuracy for memory and speed. Each size also has an English-only `.en` counterpart that tends to score better on English audio, while the multilingual checkpoints are the ones to use when translating non-English speech into English. Practical VRAM needs range from roughly 1 GB for tiny up to about 10 GB for large, so the smaller checkpoints run comfortably on consumer hardware or even CPU, while large models are usually paired with a GPU.

Because Whisper is a model and a Python package rather than a hosted product, there is no dashboard, account or quota. You install it, point the command line at audio files, and write your own integration on top. It supports many audio formats through ffmpeg, and its per-language accuracy varies widely — strong for high-resource languages, weaker for low-resource ones — which is worth knowing before adopting it for production transcription.

**Pricing & Plans:** Free and open source. The code and weights are published on GitHub under the MIT license with no usage fees or per-minute charges, though you pay for whatever compute you run it on and any OpenAI API usage if you pair it with their hosted services.

- **Multitask single model:** One Transformer handles transcription, speech translation into English, language identification and voice activity detection, selected through special task tokens.
- **Multiple model sizes:** Choose from tiny, base, small, medium, large and turbo checkpoints, with English-only .en variants, to balance accuracy against VRAM and speed.
- **Broad language coverage:** Multilingual checkpoints transcribe and translate dozens of languages, with automatic language detection on mixed-language audio.
- **Command-line and Python API:** Install via pip with openai-whisper and transcribe files from the terminal or from your own Python code, with ffmpeg handling audio decoding.
- **Permissive MIT license:** Freely embed, modify and redistribute the code and weights in commercial and personal projects without licensing fees.

**Strengths:** Completely free and open source under a permissive MIT license; Strong multilingual accuracy across many languages with automatic detection; Runs locally, so audio never has to leave your hardware; Several model sizes let you match accuracy to available compute

**Limitations:** No hosted UI, account or support — you must install and operate it yourself; Large models need substantial VRAM and a GPU for reasonable speed; Accuracy drops noticeably on low-resource languages and noisy audio; Setup requires Python, PyTorch and ffmpeg, which can be fiddly for non-developers

[Profile (Markdown)](https://openaitools.app/tool/whisper-github.md)

## Frequently Asked Questions

### How do DeepSeek and Whisper (OpenAI) compare in monthly traffic?

DeepSeek has 372,761,591 monthly visits, while Whisper (OpenAI) has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

DeepSeek has a month-over-month growth rate of +4.3%; Whisper (OpenAI) has —.

### How does pricing compare?

DeepSeek: Freemium. Free web and mobile chat app at chat.deepseek.com; pay-as-you-go OpenAI-compatible API with off-peak rates at $0.15/1M input ($0.003 cache hit) and $0.60/1M output for deepseek-flash (V4.1-Flash), and $0.66/1M input ($0.022 cache hit) and $1.98/1M output for deepseek-v4-pro (peak weekday UTC hours bill at 2x). Whisper (OpenAI): Free. Free and open source. The code and weights are published on GitHub under the MIT license with no usage fees or per-minute charges, though you pay for whatever compute you run it on and any OpenAI API usage if you pair it with their hosted services.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [ChatGPT](https://openaitools.app/tool/chatgpt-4) | 6,048,022,723 | +2.2% | Freemium | OpenAI's flagship multimodal assistant powered by GPT-6 and GPT-5.6 for reasoning, coding, and deep research. | [.md](https://openaitools.app/tool/chatgpt-4.md) |
| 2 | [Google Gemini](https://openaitools.app/tool/gemini-gemini-advanced) | 2,637,398,242 | -0.1% | Freemium | Google's multimodal AI assistant powered by Gemini 4 Carbon (Gemini Carbon 4), Gemini 4 Argon, and Gemini 3.8 Flash with a 1M-token context window and Workspace integration. | [.md](https://openaitools.app/tool/gemini-gemini-advanced.md) |
| 3 | [OpenAI Platform](https://openaitools.app/tool/openai) | 234,678,639 | -9.6% | Paid | Enterprise developer platform and API for deploying GPT-6 Astra, Sol, Luna, and realtime multimodal agents. | [.md](https://openaitools.app/tool/openai.md) |
| 4 | [Google AI Studio](https://openaitools.app/tool/google-ai-studio) | 112,620,020 | +2% | Freemium | Google's rapid prototyping IDE and developer API workbench for building multimodal applications with Gemini models. | [.md](https://openaitools.app/tool/google-ai-studio.md) |
| 5 | [Perplexity](https://openaitools.app/tool/perplexity-ai) | 88,310,223 | -3.7% | Freemium | Real-time AI answer engine and research workspace combining live web citations with frontier reasoning models. | [.md](https://openaitools.app/tool/perplexity-ai.md) |
| 6 | [Qwen 4 (Alibaba Qwen4)](https://openaitools.app/tool/qwen-4) | 35,101,353 | -6.2% | Freemium | Alibaba Cloud's 4th-generation multimodal AI assistant and foundation model lineup (Qwen4-Max, Qwen4-Plus, Qwen4-Flash & open-weight Qwen4-27B) with 1M context. | [.md](https://openaitools.app/tool/qwen-4.md) |
| 7 | [TurboScribe](https://openaitools.app/tool/turboscribe-ai) | 27,661,771 | +2.1% | Freemium | Unlimited AI transcription of audio and video in 98+ languages | [.md](https://openaitools.app/tool/turboscribe-ai.md) |
| 8 | [Airtable AI Assistant](https://openaitools.app/tool/airtable-ai-assistant) | 24,917,910 | -3.5% | Freemium, Free Trial, Paid, Contact for Pricing | Deploy AI agents and build apps conversationally on top of your own data | [.md](https://openaitools.app/tool/airtable-ai-assistant.md) |
| 9 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | One API key for hundreds of AI models from every major provider | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 10 | [Kimi AI](https://openaitools.app/tool/kimi-chat) | 19,632,261 | -23.4% | Free | Moonshot AI's long-context assistant for research, coding, and agentic work | [.md](https://openaitools.app/tool/kimi-chat.md) |

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Source: https://openaitools.app/compare/deepseek-vs-whisper-github

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